Asthma is one of the most common chronic, pulmonary diseases in the world, found in both adults and children. In all parts of the world asthma is a fast growing disease. The prevalence of asthma increases as communities adopt western lifestyles and become urbanized.
According to the latest report from the Global Initiative for Asthma (GINA) it is estimated that as many as 300 million people of all ages and all ethnic backgrounds suffer from asthma, and that the burden of this disease to governments, health care systems, families, and users is increasing worldwide.
It is also estimated, that there may be an additional 100 million persons suffering from asthma by year 2025.
Asthma is considered a chronic pulmonary disease. As of today no cure exists. However, stabilizing the disease with the right medication at the right time may preserve the life quality for human beings.
Pre and post diagnosis and home monitoring of the pulmonary function is instituted by the general practitioner accordingly. This includes repeated daily measurements of the pulmonary function and logging of data and symptoms, which is considered essential for the general practitioner to make a precise diagnosis, choose the right treatment, and decide on the medication.
Diagnosed asthmatics are very dependent upon an acceptable control of their disease and the management of the day-to-day adjustment of their medication.
Well-known problems with asthmatic home monitoring today are users lack of compliance, wrong use and lack of precision in the devices used for the home measurements, causing reduced life quality for the user when asthma is not stabilized.
This invention reduces errors in home measurements. The device uses a network of Sound Sensors and a self-correlating system in an Artificial Neural Network to analyze the collected physiological data originating from the behavior pattern of a specific user. The device compares these data with data previously collected and stored by the device about the same user and/or loaded calibrated information stored about the user and/or loaded reference information based upon information from a background population. This generates a unique and accurate picture of the user's disease based on the behavior pattern.
The recorded data is stored in the devices storage unit continuously with a set of date and time registration details. To shorten learning time for artificial neural network unit, it is possible to upload user data to the device. Pre-measured data can also be uploaded to the device.
The interpretation of behavior pattern is improved by logging of the user condition and making suggestions to support the self-control of medicine to take.
This invention also deals with a cooperative calculation approach for using artificial neural network ensembles and applies multi objective optimization.
Cooperative calculation approach is a recent paradigm in evolutionary computation that allows through a learning process to model the lungs sound and the acoustic of respiratory passage and its cooperative environments for a specific user in relation to itself. Although processing algorithms that make the device able to handle with its artificial neural network and with a sufficient number of neurons in the hidden layer would suffice to solve user diseases behavior pattern and indicate or alarm the appearance of a given condition for the user for example an asthmatic attack.